课程: Probability Foundations for Data Science

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Central Limit theorem

Central Limit theorem

课程: Probability Foundations for Data Science

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Central Limit theorem

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- [Narrator] Let's explore the central limit theorem. The central limit theorem states the distribution of sample means from a large sample of independent and identically distributed variables is approximately normally distributed. This is the case regardless of the population distribution being normally distributed or not. Why is this important? The central limit theorem is important because it allows you to use normal distribution calculations with your distribution. This theorem works whether the population distribution is normally distributed or not. This is also useful for conducting hypothesis testing and confidence interval estimation. So how many samples is enough? Mathematicians have decided 30 is the magic number of samples to be a sufficiently large sample, and when you increase the number of samples, the sample means will become more and more normally distributed. You can technically use the central limit theorem on smaller sample sizes, but note that it will not be as…

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